Research

Depression is the leading cause of disability worldwide. Transcranial magnetic stimulation (TMS) promises a unique technological approach to the therapy. Repetitive TMS protocols can rapidly induce lasting potentiation or inhibition of neural circuits, earning first FDA approval in 2008. Nevertheless, a major remaining challenge is a variability of outcomes, with ~35% of patients not responding to the treatment. We are developing spatiotemporally precise, personalized TMS paradigms for next-generation therapy of depression. Our closed-loop stimulation with an electroencephalography (EEG) approach uses real-time biosignals to track ongoing brain states and optimize the stimulation. Further, we are exploring neuroimaging (MRI) guided interventions with computational head modeling for the anatomically and functionally specific stimulation delivery. Finally, we conduct EEG biomarker research to better understand the TMS treatment mechanisms and develop prospective patient stratification frameworks.

We are developing transdiagnostic techniques to advance non-invasive neurostimulation by integrating transcranial magnetic stimulation (TMS) with real-time recordings and analyses of high-density broad spectrum electroencephalography (EEG) and other multimodal biosignals (ExG). Our research objectives include developing robust real-time digital signal processing and optimization algorithms for temporal personalization of brain stimulation, developing collaborative robotics (Cobots) and neuronavigation frameworks for 6 degrees-of-freedom spatial precision, implementing finite element analysis (FEA) for advanced stimulation dozing, and analyzing functional EEG and magnetic resonance imaging (MRI) information for individual retrospective and prospective markers of optimal interventions.

We are specifically interested in the connectivity and disconnectivity in large-scale brain communications, expressed as brain oscillations, and their role as a vehicle for higher-order executive functions. Computationally optimized manipulations of brain oscillations using transcranial alternating current stimulation (tACS) can safely modulate local brain oscillations and, when applied at multiple sites, complex connectivity processes such as cross-frequency coupling and neocortical traveling waves. We are studying the mechanisms and novel forms of tACS and their experimental applications in human cognitive research and patients with mental health conditions.

Computational modeling of transcranial brain stimulation, such as electromagnetic finite element analysis (FEA), provides an “invasive” outlook on the actions of non-invasive neuromodulation across the brain tissues. Since computational tools for TMS and tACS were validated, they became essential to the ad hoc and post hoc analyses of the brain stimulation experiments. We are continuing the development of computational frameworks across two main directions. First, the utility of individual head modeling based on structural and diffusion-tensor MRI for stimulation dose optimization. Second, applications of populational modeling for meta-analysis of diverse experimental trials according to their physical mechanisms.